An empirical application of Particle Markov Chain Monte Carlo to frailty correlated default models

This paper aims to evaluate the likelihood of corporations’ defaults based on data of U.S. public non-financial firms over the period January 1980–June 2019 by incorporating both observable firm-specific/macroeconomic factors and latent factors. We use a frailty correlated default model introduced in Duffie et al. (2009) and adopt a Particle Markov Chain Monte Carlo (Particle MCMC) method to handle the hidden factors. A horse race between our method and the method proposed by Duffie et al. (2009) shows that our approach outperforms theirs at predicting the frailty correlated default risk. Our empirical results show that the variation of the default rates of U.S. industrial firms can be significantly explained by both observable and hidden factors.